Price markdown apparatus

ABSTRACT

An apparatus for providing relative optimized pricing for markdown items is provided. A financial engine for determining revenue for determining sales costs is provided. An optimization engine receiving input from the financial engine and uses the determined sales costs to provide relative optimized pricing for markdown items and provides pricing and a promotion calendar for non-markdown items.

BACKGROUND OF THE INVENTION

The present invention relates a device for providing markdown prices.

In retail businesses, prices of various products must be set. Suchprices may be set with the goal of maximizing margin or demand or for avariety of other objectives. Margin is the difference between totalrevenue and costs. Total sales revenue is a function of demand andprice, where demand is a function of price. Demand may also depend onthe day of the week, the time of the year, the price of relatedproducts, location of a store, the location of the products within thestore, advertising and other promotional activity both current andhistorical, and various other factors. In addition, demand is alsoheavily influenced by the seasonal patterns and holidays. As a result,the function for forecasting demand may be very complex. Costs may befixed or variable and may be dependent on sales volume, which in turndepends on demand. As a result, the function for forecasting margin maybe very complex. For a chain of stores with tens of thousands ofdifferent products, identifying the relevant factors for each productand store, then determining a function representing that demand aredifficult. The enormous amount of data that must be processed for suchdeterminations is too cumbersome even when done by computer. Further,the methodologies used to forecast demand and the factors thatcontribute to it require the utilization of non-obvious, highlysophisticated statistical processes.

Such processes are described in U.S. Pat. No. 7,062,447, entitledIMPUTED VARIABLE GENERATOR, filed Dec. 20, 2000 and issued Jun. 13, 2006by Valentine et al., and U.S. patent application Ser. No. 09/741,958,entitled PRICE OPTIMIZATION SYSTEM, filed Dec. 20, 2000 by Venkatramanet al., which both are incorporated by reference for all purposes.

A markdown is a schedule of known price reductions taken over arelatively short time interval with the express purpose of managing aproduct out the assortment gracefully and cost-effectively. Markdownsoccur for various reasons including planned assortment transitions orresets, product obsolescence and discontinuance brought on by changes intechnology, taste, law and a myriad of other factors that affectconsumer demand. Category resets and assortment changes are by far themajor drivers of markdown pricing.

Markdown pricing is an important consideration for retailers who sellseasonal, fashion, or other short-life-cycle products. “Clearancemarkdown dollars” which equal the revenue that would be generated atregular price minus the actual dollars obtained from marked-down items,often amount to several million dollars per year for major retailchains. While markdown discounts boost sales, they erode profits. Takinginto account generally thin margins for the majority of retailers, theimplementation of optimal markdown policies (that is, the timing andmagnitude of markdowns) becomes crucial.

In addition, markdown pricing is a more highly focused specificgoal-driven activity than base price and promotion. It also tends to bemore tactical in nature, liquidating inventory over a specific timeperiod. The objective of pricing during the clearance period isdifferent from base pricing or promotions. Although the business task ofpricing is the same, the approach, importance and decision criteria forpricing during the clearance period is different.

SUMMARY OF THE INVENTION

To achieve the foregoing and other objects and in accordance with thepurpose of the present invention an apparatus for providing relativeoptimized pricing for markdown items is provided. A financial engine fordetermining revenue for determining sales costs is provided. Anoptimization engine receiving input from the financial engine and usesthe determined sales costs to provide relative optimized pricing formarkdown items and provides pricing and a promotion calendar fornon-markdown items.

In another manifestation of the invention, a computer implemented methodfor providing pricing for markdown items and non-markdown items isprovided. Sales, cost, and competitive data from the plurality of storesare collected. The collected sales, cost, and competitive data areanalyzed. Pricing and a markdown schedule for markdown items and pricingfor non-markdown items are provided. On an at least weekly basis, salesof the markdown items are analyzed. The markdown schedule is tuned basedon the analyzing data collected on an at least weekly basis. A tunedmarkdown schedule is provided.

These and other features of the present invention will be described inmore detail below in the detailed description of the invention and inconjunction with the following figures.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention is illustrated by way of example, and not by wayof limitation, in the figures of the accompanying drawings and in whichlike reference numerals refer to similar elements and in which:

FIG. 1 is a high level schematic view of an optimizing system.

FIGS. 2A-C are flow charts of a process that uses the optimizing system.

FIG. 3 is a schematic view of an econometric engine.

FIG. 4 flow of an example of data transformation and demand coefficientgeneration.

FIG. 5 is a flow chart of a rule relaxation process that may be used bythe invention.

FIGS. 6A and 6B are views of a computer system that may be used in anembodiment of the invention.

FIG. 7 is a more detailed view of an optimization engine.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

The present invention will now be described in detail with reference toa few preferred embodiments thereof as illustrated in the accompanyingdrawings. In the following description, numerous specific details areset forth in order to provide a thorough understanding of the presentinvention. It will be apparent, however, to one skilled in the art, thatthe present invention may be practiced without some or all of thesespecific details. In other instances, well known process steps and/orstructures have not been described in detail in order to notunnecessarily obscure the present invention.

The invention provides a markdown optimization system that providesmarkdown prices.

Optimization System

To facilitate understanding, an embodiment of the a markdownoptimization system which provides markdown pricing will be described.The purpose of the markdown optimization system is to receive raw datathat relates to a specified econometric problem; and to use coefficientsgenerated by econometric modeling engine that represents the significantfactors affecting the behaviors represented by the data; and to generatea set of markdown prices realizing user goal (profit, sales etc) whileobeying various business constraints (observing inventory levels,observing cadence of prices suggested by the user etc). In one example,the econometric engine produces coefficients that represent the drivingfactors for consumer demand, synthesized from sales volume and otherretail-business related data inputs.

The purpose of the Markdown optimization model is to help retailersdecide what the markdown schedule should be for a given product or setof products. Retailers typically do markdowns (or discounts) at the endof a product's lifecycle (Product lifecycle refers to the naturalevolution of product demand in the market. For example, an item mightstart out with a low demand, then pick up demand as it gets popular andthen finally decline as it is no longer desired by the consumers) inorder to move that product out of inventory. In the past, this hastypically been done utilizing a fixed schedule of markdowns based on theretailer's intuition and the need to move the product out of inventory.The preferred embodiment of the invention provides optimalrecommendations that accounts for related merchandising activitiesstore-wide or within the category. This consideration becomes importantwhen only a subset of related or like items are being considered formarkdowns and another subset is being actively promoted.

FIG. 1 is a schematic view of an optimizing system 100 using aprocessing system 103. The processing system 103 comprises a first datatransformation engine 101, a second data transformation engine 102,econometric engine 104, a financial model engine 108, an optimizationengine 112, and a support tool 116. The econometric engine 104 isconnected to the optimization engine 112, so that the output of theeconometric engine 104 is an input of the optimization engine 112. Thefinancial model engine 108 is connected to the optimization engine 112,so that the output of the financial model engine 108 is an input of theoptimization engine 112. The optimization engine 112 is in two-waycommunications with the support tool 116 so that output of theoptimization engine 112 is provided as input to the support tool 116.The support tool 116 is in two-way communication with a planner 117, whois a user. The planner 117 may use the support tool to generate a plan118. The plan 118 is implemented by the stores 124.

FIG. 2A is a high level flow chart of an optimizing process that usesthe optimizing system 100. An optimization is performed (step 202).

FIG. 2B is a more detailed flow chart of the optimization (step 202).Data 120, 132 is provided from the stores 124 to the first datatransformation engine 101 and the second data transformation engine 102,where the data from the stores is transformed (step 204). Generally, thedata provided to the first data transformation engine 101 and the seconddata transformation engine 102 may be point-of-sale information, productinformation, and store information. The transformed data from the firstdata transformation engine 101 is then provided to the econometricengine 104. The econometric engine 104 processes the transformed data toprovide demand coefficients 128 (step 208) for a set of algebraicequations that may be used to estimate demand (volume sold) givencertain marketing conditions (i.e. a particular store in the chain),including a price point.

FIG. 4 is a more detailed the flow chart of an example of animplementation steps 204 and 208 which provides markdown optimization,starting from receiving customer data, data transformations, and runningthe markdown econometric model. Specifically, customer provides the POSdata that is stored in multiple databases—modeling and optimizationdatabases (step 404). The raw data in the modeling database undergoesmultiple transformations (such as error checking, corrections formissing/incorrect items, custom aggregations, dividing dataset intodifferent subsets etc) (step 408). At this point, data is ready to runthe econometric model.

The econometric modeling (step 412) includes estimations forseasonality, price elasticity, and product life-cycle terms. Further, itshows how incorrect coefficients are flagged and corrected for by avariety of means (re-clustering, enforcing correct sign etc).

After completing modeling (which results into generation of all thedemand coefficients), it passes through an assess model thatautomatically determines the overall quality of the model by comparisonwith actual data (forecast comparison exercise) and additionally, itprovides reports on the quality in different forms (step 416).

After completing the above steps, a model is deemed to be ready forproduction use. At this point, the coefficients and other relevantquantities are passed to the optimization database, with which the userinteracts via GUI to obtain optimal markdown recommendations.

The demand coefficients 128 are provided to the optimization engine 112(step 212). Additional processed data from the econometric engine 104may also be provided to the optimization engine 112. The financial modelengine 108 may receive transformed data from the second datatransformation engine 102 (step 216) and processed data from theeconometric engine 104. The transformed data is generally cost relateddata, such as average store labor rates, average distribution centerlabor rates, cost of capital, the average time it takes a cashier toscan an item (or unit) of product, how long it takes to stock a receivedunit of product and fixed cost data. The financial model engine 108 mayprocess the data to provide a variable cost and fixed cost for each unitof product in a store (step 220). The processing by the econometricengine 104 and the processing by the financial model engine 108 may bedone in parallel. Cost data 136 is provided from the financial modelengine 108 to the optimization engine 112 (step 224). The optimizationengine 112 utilizes the demand coefficients 128 to create a demandequation. The optimization engine is able to forecast demand and costfor a set of prices to calculate net profit (margin).

Various price and promotion optimizations may be used, in variousembodiments, such as the price and promotions described in thereferences cited above. These optimizations are combined with a markdownoptimization, which will be described in more detail below.

A price, promotion, and markdown plan is then generated (step 244). Aprice, promotion, and markdown plan sets prices for regular items,provides a promotional calendar for promotional items, and provides amarkdown schedule for markdown items. By providing these three items ina single plan, an integrated plan is provided that optimizes price,promotion, and markdown, so that they do not interfere with each other.In order to generate a plan, the planner 117 provides to the supporttool 116 optimization rules. The optimization engine 112 may use thedemand equation, the variable and fixed costs, and the rules to computean optimal set of prices that meet the rules. FIG. 2C is a more detailedflow chart of the step of generating a price, promotion, and markdownplan used in an embodiment of the invention. In this embodiment, a priceand promotion plan is first generated (step 272). The price andpromotion plan provides optimized pricing and a promotion plan. Anoptimized markdown plan is then generated based on the price andpromotion plan (step 276). The planner 117 may be able to providedifferent sets of rules to create different scenarios to determinedifferent “What if” outcomes. From the various scenarios and outcomes,the planner is able to create a plan.

For example, if a rule specifies the maximization of profit, theoptimization engine would find a set of prices that cause the largestdifference between the total sales and the total cost of all productsbeing measured. If a rule providing a promotion of one of the productsby specifying a discounted price is provided, the optimization enginemay provide a set of prices (both the promoted prices and the markdownprices) that allow for the promotion of the one product and themaximization of profit under that condition. A rule may provide for amarkdown of an item, where the markdown maximizes revenue while allowingfor the item to be no longer sold by a certain deadline. In thespecification and claims, the phrases “optimal set of prices” or“preferred set of prices” are defined as a set of computed prices for aset of products where the prices meet all of the rules. The rulesnormally include an optimization goal, such as optimizing profit oroptimizing volume of sales of a product and constraints such as a limitin the variation of prices and other time-based limitations (such asminimum time between discounts). The optimal (or preferred) set ofprices is defined as prices that define a local optimum of aneconometric model, which lies within constraints specified by the rules.When profit is maximized, it may be maximized for a sum of all measuredproducts. Such a maximization, may not maximize profit for eachindividual product, but may instead have an ultimate objective ofmaximizing total profit.

By optimizing non-markdown item pricing, promotion scheduling, andmarkdown scheduling, a master merchandizing calendar may be generatedthat shows all pricing scenarios and events with pre-season andin-season activity. This ensures maximum forecast accuracy incorporatingall cannibalization and halo effects in a store. One result may be thereduction of markdowns as markdown optimization is integrated withpricing and promotion planning. In addition, the master merchandizingcalendar may be used to manage conflict between price optimization,promotion plans, and markdown plans.

For a price optimization plan, the optimal set of prices is the plan.The plan may be for a long term. For example, the plan may set weeklyprices for the next six months.

The plan is then implemented (step 248). This may be done by having theplanner 117 send the plan 118 to the stores 124 so that the stores carryout the plan. In one embodiment, the support tool provides a graphicuser interface that provides a button that allows the planner toimplement the plan. The support tool would also have software to signalto the stores to implement the plan. In another embodiment, software ona computer used by the planner would integrate the user interface of thesupport tool with software that allows the implementation of the plandisplayed by the support tool by signaling to the stores to implementthe plan.

The results of the plan are measured with updated data (step 252).Updated data may be provided on a weekly or daily basis. The updateddata may be sent to the processing system 103.

The updated data is used to generate a tuning recommendation (step 256).This may be done in various ways. One way is by generating a new plan,which may be compared with the long range plan. Another way may be touse the updated data to see how accurate the long range plan was foroptimization or for prediction of sales. Other data may be measured todetermine if tuning should be recommended without modeling the updateddata.

In one embodiment the detection of changes to externally defined costand competitive price information, and updates to the plan required tomaintain business rule conformance are used as factors to determinewhether tuning is needed. To detect such factors the econometric modelis not needed, but instead other factors are used. The econometric modelmay then be updated based on such changes to “tune” the optimized planfor changing conditions

In another embodiment, tuning is performed when certain thresholdconditions are reached—i.e. changes are substantial enough to materiallyimpact the quality of the previously optimized plan. Other exampleswhere tuning is required includes a rapid change in the fashion trendcausing corresponding change in the demand, change in weather or seasoncausing a significant change in demand, and change in the timing of thecategory/product reset decision. In such processes, the econometricmodel may be used to provide predictions and then compared to actualdata.

The system is able to provide a tuning recommendation (step 260). Thismay be implemented by setting a range or limits either on the dataitself or on the values it produces. In the first case, if changes tothe updated data relative to the original data exceed a limit or movebeyond a certain range, a flag or other indicator may be used torecommend tuning to the user. In the second case, if the updated datacreates prediction errors beyond the specified range or limits, a flagmay be used to recommend tuning to a user.

For example, a competitor price index may be used in the optimizationand in generation of a tuning indicator. A competitor price index is anormalized index of competitor prices on a set of items sold at a set oflocations in relation to those provided by the plan, using competitorprice data that is provided through various services. As a specificexample, a user might define a competitor price index on all brands andsizes of paper towels sold at stores with a retail chain A located lessthan five miles away (the identification of retail chain a locationswould be done outside the system). An indicator can then be provided toidentify when prices provided by the plan exceed a competitor priceindex of 105—in other words when they are above the competitor's pricesby more than 5% on some subset of items (in the case above, when retailchain A has lowered paper towel prices, resulting in a change to thatcompetitor price index relative to the plan). In another example, costsare always changing. It is usually undesirable to change pricesimmediately every time costs change. Therefore, in another example, thesystem provides a tuning recommendation when either small cost changescause an aggregate change of more than 5% or a single cost change causesa cost change of more than 3%. Therefore, the tuning indicators arebased on formulas that measure either changes in individual data orchanges in relationships between values of the data.

In another example, the user might define triggers on the level of salesachieved against the target. Thus, if the actual sales are below athreshold (say of 25%) below the projected sales, it will trigger are-optimization or a re-prediction based upon the updated sales andinventory information. This might also trigger a re-estimation of theeconometric model (if warranted) to capture changes in consumer demandsince the previous model estimation.

In viewing the re-predicted outcome and the tuning recommendation, theplanner 117 is able to have the processing system 103 tune the plan(step 264). The planner 117 may then send out a message to implement thetuned plan (step 248). A single screen may show both the informationthat the planner needs to use to make a decision and provide a button toallow the planner to implement a decision. The button may also allowtuning on demand, whenever desired by the user.

This process allows for a long term plan to be corrected over the shortterm. For markdown planning, this tuning is especially important. Thisallows for corrections if the long term plan has an error, which in theshort term may be less significant, but over the long term may be moresignificant. The tactical nature of markdown process makes it criticalthat these models and optimizations are examined and tuned very rapidly,perhaps once every week or more often. These changes are based uponobserved changes in the demand that would necessitate a change inmarkdown strategy to meet the original goals. This would further entailpossibly running markdown model estimations frequently to re-adjust thefunctional relationship for the latest changes. In addition, currentevents may change the accuracy of a long term model. Such current eventsmay be a change in the economy or a natural disaster. Such events maymake a six-month plan using data from the previous year less accurate.The ability to tune the plan on at least a weekly basis with data fromthe previous week makes the plan more responsive to current events.

The optimization system provides a promotional plan that plans andschedules product promotions. Without the optimization system,poor-performing promotions may go unidentified until it is too late tomake changes that materially affect their performance. The use ofconstant updates helps to recognize if such a plan creates businessproblems and also allows a short term tuning to avoid further damage.For example, a promotion plan may predict that a discount coupon for aparticular product for a particular week will increase sales of theproduct by 50%. A weekly update will within a week determine theaccuracy of the prediction and will allow a tuning of the plan if theprediction is significantly off.

The invention provides markdown pricing. The use of constant updates maybe used to both manage a product out of the assortment and maximizerevenue using markdown prices. The invention provides an integratedpricing program that for setting prices, setting promotions, and settingmarkdowns, so that prices, promotions, and markdowns may be optimizedtogether.

In one embodiment, the system allows an automatic tuning when a longterm plan is not accurate within a certain percentage. In anotherembodiment, the planner may be allowed to decide whether the long termplan is in enough agreement with the updated data so that the long termplan is kept without tuning. In addition, other notification elementsmay exist in the system allowing the user to set up customized alertsbased upon his/her chosen criteria. The customization includes settingpriority based upon a set of merchandising outcomes such as levels orchanges in levels of sales, revenues, etc. These alerts may be deliveredto the user in multiple ways (e.g. email, pager, text message etc). Thisallows the user to take the desired action.

Thus, the invention allows the integration between the operationalsystem of a business, which sets prices, promotions, and markdowns andperforms other sales or business functions, with the analytical systemof a business which looks at sales or other performance information, toallow a planner to receive timely analytical information and then changethe operational system and then to quickly, through the analyticalsystem, see the results of the change in the operational system todetermine if other changes in the operational system need to be made.

Such a constant tuning of a plan is made difficult by the large amountof data that needs to be processed and the complexity of the processing,which could take weeks to process or would be too expensive to processto make such tuning profitable. U.S. patent application Ser. No.11/365,153, filed Feb. 28, 2006, entitled, “PLAN TUNING ENGINE”, by AlanColeman et al., U.S. patent application Ser. No. 11/364,966, filed Feb.28, 2006, entitled, “SCALABLE TUNING ENGINE”, by Alan Coleman et al.,and U.S. patent application Ser. No. 11/365,634, filed Feb. 28, 2006,entitled, “COMPUTER ARCHITECTURE”, by Alan Coleman et al., U.S. all ofwhich are incorporated by reference for all purposes, discloses a systemfor allowing the constant tuning of a plan. A price optimization systemis described in U.S. patent application Ser. No. 09/741,958, filed Dec.20, 2000, entitled “PRICE OPTIMIZATION SYSTEM”, by Krishna Venkatramanet al., which is incorporated by reference. A system for creating anoptimized promotion event calendar is described in U.S. patentapplication Ser. No. 09/849,738, filed May 5, 2001, entitled “SYSTEM FORCREATING OPTIMIZED PROMOTION EVENT CALENDAR”, by Krishna Venkatraman etal., which is incorporated by reference. An Econometric Engine system isdescribed in U.S. patent application Ser. No. 09/741,956, filed Dec. 20,2000, entitled “ECONOMETRIC ENGINE”, by Hau Lee et al., which isincorporated by reference. A system for providing rule relaxation isdescribed in U.S. patent application Ser. No. 09/888,340, filed on Jun.21, 2001, entitled “PRICE OPTIMIZATION WITH RULE RELAXATION”, by MichaelNeal et al., which is incorporated by reference.

Data 120 is provided to the processing system 103. The data 120 may beraw data generated from cash register data, which may be generated byscanners used at the cash registers. The first data transformationengine 101 and the second data transformation engine 102 format the dataso that it is usable in the econometric engine and financial modelengine. Some data cleansing, such as removing duplicate entries, may beperformed by the first and second data transformation engine 101, 102.

FIG. 3 is a more detailed view of the econometric engine 104. Theeconometric engine comprises an imputed variable generator 304 and acoefficient estimator 308. The formatted data from the first datatransformation engine 101 is provided to the imputed variable generator304. The imputed variable generator 304 generates a plurality of imputedeconometric variables.

FIG. 7 is a more detailed view of an optimization engine 112 that may beused in an embodiment of the invention. The optimization engine 112comprises a price optimization engine 704 that optimizes prices ofnon-markdown items, a promotion and calendar optimization engine 708,and a markdown optimization engine 712.

FIGS. 6A and B illustrate a computer system 600, which may be suitablefor implementing embodiments of the present invention. FIG. 6A shows onepossible physical form of the computer system. Of course, the computersystem may have many physical forms ranging from an integrated circuit,a printed circuit board, and a small handheld device up to a huge supercomputer. Computer system 600 includes a monitor 602, a display 604, ahousing 606, a disk drive 608, a keyboard 610, and a mouse 612. Disk 614is a computer-readable medium used to transfer data to and from computersystem 600.

FIG. 6B is an example of a block diagram for computer system 600.Attached to system bus 620 is a wide variety of subsystems. Processor(s)622 (also referred to as central processing units, or CPUs) are coupledto storage devices, including memory 624. Memory 624 includes randomaccess memory (RAM) and read-only memory (ROM). As is well known in theart, ROM acts to transfer data and instructions uni-directionally to theCPU and RAM is used typically to transfer data and instructions in abi-directional manner. Both of these types of memories may include anysuitable of the computer-readable media described below. A fixed disk626 is also coupled bi-directionally to CPU 622; it provides additionaldata storage capacity and may also include any of the computer-readablemedia described below. Fixed disk 626 may be used to store programs,data, and the like and is typically a secondary storage medium (such asa hard disk) that is slower than primary storage. It will be appreciatedthat the information retained within fixed disk 626 may, in appropriatecases, be incorporated in standard fashion as virtual memory in memory624. Removable disk 614 may take the form of any of thecomputer-readable media described below.

CPU 622 is also coupled to a variety of input/output devices, such asdisplay 604, keyboard 610, mouse 612, and speakers 630. In general, aninput/output device may be any of: video displays, track balls, mice,keyboards, microphones, touch-sensitive displays, transducer cardreaders, magnetic or paper tape readers, tablets, styluses, voice orhandwriting recognizers, biometrics readers, or other computers. CPU 622optionally may be coupled to another computer or telecommunicationsnetwork using network interface 640. With such a network interface, itis contemplated that the CPU might receive information from the network,or might output information to the network in the course of performingthe above-described method steps. Furthermore, method embodiments of thepresent invention may execute solely upon CPU 622 or may execute over anetwork such as the Internet in conjunction with a remote CPU thatshares a portion of the processing.

In addition, embodiments of the present invention further relate tocomputer storage products with a computer-readable medium that havecomputer code thereon for performing various computer-implementedoperations. The media and computer code may be those specially designedand constructed for the purposes of the present invention, or they maybe of the kind well known and available to those having skill in thecomputer software arts. Examples of computer-readable media include, butare not limited to: magnetic media such as hard disks, floppy disks, andmagnetic tape; optical media such as CD-ROMs and holographic devices;magneto-optical media such as floptical disks; and hardware devices thatare specially configured to store and execute program code, such asapplication-specific integrated circuits (ASICs), programmable logicdevices (PLDs) and ROM and RAM devices. Examples of computer codeinclude machine code, such as produced by a compiler, and filescontaining higher level code that are executed by a computer using aninterpreter. Computer readable media may also be computer codetransmitted by a computer data signal embodied in a carrier wave andrepresenting a sequence of instructions that are executable by aprocessor.

Demand groups are groups of highly substitutable products, such asdifferent sizes or brands of the same product or alternative products.Determination of demand groups is a complex process that leverages boththe domain knowledge and inferred statistical relationships betweenproducts. Further, demand groups may differ from retailer to retailersince it also depends upon the exact product assortment being carried bya retail chain.

In the specification, examples of product are not intended to limitproducts covered by the claims. Products may for example include food,hardware, software, real estate, financial devices, intellectualproperty, raw material, and services. The products may be sold wholesaleor retail, in a brick and mortar store or over the Internet, or throughother sales methods.

Markdown System Characteristics

A markdown is a schedule of known price reductions taken over arelatively short time interval with the express purpose of managing aproduct out the assortment gracefully and cost-effectively. Markdownsoccur for various reasons including planned assortment transitions orresets, product obsolescence and discontinuance brought on by changes intechnology, taste, law and a myriad of other factors that affectconsumer demand. Category resets and assortment changes are by far themajor drivers of markdown pricing.

A category reset is a major change in the assortment of a category orgroup of products during which managers, merchants, and buyers remove asubstantial fraction of products in the category from the assortment andreplace them with new products. The products that they select fromremoval are usually those that aren't selling well, have beendiscontinued, or have been replaced by a newer technology, whereas thereplacements typically represent newer technology (consumerelectronics), line extensions and re-packaging of existing products.

Products that exit the assortment during these resets do so in threeways. They may be returned to the vendor (RTV), they may be sold toalternate channels (“sold sideways”), or they may be phased out in thestores via a controlled set of rapid price reductions. This lastalternative is known as markdown pricing. In consumer electronics, thenumber of such assortment transitions depends on the pace of technologychange in the product category in question, the timing of new productintroductions by key manufacturers, and, to some extent by seasonal andholiday factors. In the case of apparel retailers, style and seasondrive these changes. At grocers category resets result from annual orsemi-annual reviews of product assortment. In general, most retailersmaintain and publish a calendar of planned resets or assortmenttransitions and support these calendars with several well definedprocesses and related data. Since resets themselves are regular episodicevents at many retailers, many of them rely on a backbone of supportingdata to manage them efficiently. The markdown process should ensure thatthe users of the product are not burdened with looking for and managingthis supporting data.

For many retailers, markdown pricing is one of three ways they use toremove products from the assortment. A markdown plan is a schedule ofknown price reductions over a short time interval and is designed tosell off remaining inventory in a manner consistent with the financialmetrics of the product category. A plan must accommodate several hardoperational constraints as well as a number of business rules that guidestrategy in order to be viable and practical.

Buyers will choose an objective and create a plan based on guidelinesfor the category and the set of products, and on the time available tocomplete the markdown phase. Unfortunately, buyers frequently have toplan and execute markdowns in a reactive mode. They may learn that amanufacturer intends to replace a product within a matter of 2-4 weeks.In other cases, sudden technology transitions may suddenly make aproduct considerably less attractive to consumers and require aphase-out of its inventory in short order.

While the immediate goal of markdown pricing is to sell down a set ofdiscontinued products, this goal goes hand in hand with the largerobjective of ensuring the transition between the old products and thenew entering products proceeds smoothly. This means that the assortmentchoice during the markdown cycle is respectable and consumers do notleave the store in droves because of a thin assortment.

Constraints

Operational constraints come about due to a variety of reasons.Capacitated resources like store-labor place limits on how frequentlyprices may change in stores and these limits, in turn, translate to avariety of operational constraints. For instance, the operationalcalendar at the stores may stipulate that markdown prices can changeonly on particular days of the week, and on particular dates on thecalendar. Other operational constraints may require some minimum timeperiod between successive markdowns. For instance, a retailer may have aminimum period of 2 weeks. Local, state and federal regulations mayimpose operational restrictions as well, most notably in determining theminimum price reduction required to qualify as a markdown or clearanceprice. Furthermore, once a product has been placed on clearance, thereare often legal restrictions that prevent retailers from raising theprice for an extended period of time ranging from 6 months to a year.

Business constraints arise from a host of considerations. Someconstraints like the last digit rule exist simply because it is helpfulfor an analyst to be able to distinguish between markdown prices andother regular or promotional prices. Some companies do this by requiringmarkdown prices to end in specified last digits with rules that enforcethese requirements. More commonly, though, buyers and analysts driveother business rules with a view toward financial performance during themarkdown cycle. These rules include among others: maximum markdownpercents and values that can be applied to products on each occasion,maximum total markdown percent over the entire markdown cycle, the setof allowable price points, and the maximum number of markdown occasionsduring the cycle. Markdown parameters and attributes

In the inventive system, all of these rules and constraints are definedby parameters or attributes and values that fully describe them. Theyapply to individual products and include: the planned start and enddates for the markdown period for the product, the number of markdownoccasions allowed for the product, the maximum total markdown allowedexpressed as a percent, the minimum interval between successive markdownoccasions, the salvage value of the product expressed as cents on thedollar which value is the percent of the product cost at which theleftover inventory may be sold, the set of allowable markdown prices forthe product, the default objective to use in determining the markdownplan, the allowable dates and days for markdowns, and the operationalcalendar that specifies the days and dates on which markdowns can occurand the holding cost (expressed as a percentage) which is the cost ofcapital that remains tied up in the inventory.

The merchandising hierarchies at different retailers employ differentcriteria to create and maintain product groups within the hierarchy.Some of these product groups are especially relevant to markdownplanning. For example, for Company A, which is into clothing, seasonaland style attributes generate sets of products that match a combinationof season and style levels. On the other hand, for company B, which isinto electronics, a senior buyer may use product groups consisting ofall products within a category that have a planned discontinuance datethat falls in some user-specified interval. Such product groups may becalled dynamic product groups since their membership is dynamic anddepends at any point in time on the products that meet the criteria.

While these attributes apply eventually to each product, their valuesmay be defined at several levels of a product hierarchy. For instance,the maximum markdown percent per markdown occasion may be specifiedglobally at the corporate level and applies to all products. However,this value may be over-ridden at lower levels of the hierarchy like thecategory or sub-category level, or product group level. A markdownsystem needs to allow users to establish these hierarchies.

The markdown parameter values may be redefined at the level of theseproduct groups and inherited by the products that belong to them. Ingeneral, the products inherit the value at the lowest level of theappropriate product hierarchy.

Part of the reason for this hierarchy is because various companies havedifferent people with different authority in defining markdown and otherpricing and promotion constraints. Buyers and analysts generally createand maintain multiple product and store hierarchies and use them indifferent phases of markdown planning. For instance, a junior buyer atCompany B will use at least two different store hierarchies for markdownmanagement. The first links stores to supplying DCs (distributioncenters or the warehouses from where products will be delivered to agiven store) and the second organizes stores by region. Both hierarchiesare useful and necessary for markdown planning and execution.

Similarly, buyers and planners do select the set of products formarkdown in a number of ways. They may traverse some portion of themerchandize hierarchy to select the set of products to include in themarkdown or use dynamic or custom product groups. For instance, in theapparel vertical, seasonal products may be grouped together within aseasonal product group and clustered further by size-color combinations.In this case, buyers may wish to select all products within a seasonalproduct group that match a certain color and size combination.Alternatively, in electronics, the assortment transition processproduces a list of products that are candidates to be marked down. Insuch cases, buyers may have a list of product IDs and may simply want tosearch through product catalogue for these ids, and include them in themarkdown. Finally, users also like to search through the hierarchy aswell to select products.

The retailer supply chain system collects and maintains in-store andin-warehouse inventory levels as well as the in-transit and on-orderinventory. Planners use this information to set thresholds on metricslike the remaining weeks of supply in the system. These metrics andthresholds help buyers to decide when to begin the markdown cycleitself. The inventory level and position are vital in creating andadjusting the markdown schedule since the goal of a markdown cycle is toexit the assortment gracefully.

Creating a Markdown Plan

The first step in creating a markdown plan is to name it and to bookend,the time interval with a starting date and an ending date or out-date.The starting date is the first date on which a plan may recommend amarkdown action such as a price reduction.

The next step is to select the set of products of interest and the setof stores over which the markdown is to run. At this point, there aretwo paths a buyer may take—he may create a fully specified markdown planby specifying the markdown items using the support tool, or set up anoptimization scenario with objectives, rules, and control parameters.

Defining Markdown Plan Specifications on the Support Tool:

A user may access the support tool to define markdown planspecifications. The markdown plan specifications may specify items to bemarked down, markdown time periods, and possibly how many items are tobe marked down. The markdown period may specify when the markdown beginsand a target date to end the markdown, which may either be when themarkdown item inventory is sold out or the item ceases to be sold by theretailer. A markdown plan simply requires a schedule of markdown pricesfor each store and each product under consideration. A schedule in turnhas two facets, the timing of the various price changes and the depth ofeach price change. At one extreme, a buyer may apply the same scheduleto every product and every store, and at the other, may apply adifferent schedule for each store-sku location. In practice, the happymedium is, in fact, something in between.

Buyers will typically create schedules for stores that are clustered bygeography or by warehouse. If products have distinctive selling patternsby geography or region, (beachwear for e.g.), regional clusters areappropriate, whereas if products have substantial warehouse inventory atthe beginning of the markdown cycle, buyers will opt to set a commonschedule across stores that share common warehouses. Sometimes buyersmay choose to cluster stores because doing so reduces the practicaloperational impact of pricing within the store. Stores have to implementmarkdown prices fairly rapidly and if the number of store-sku pricechanges multiplies rapidly, the labor cost and resources will overwhelmthe capacity of the stores to implement the changes.

Buyers may also cluster products and assign a common schedule to eachcluster. This happens, most notably, when products fall into price bandsand buyers wish to allow different price points and percent discounts toproducts in each price band. In such cases, buyers will create adifferent schedule for each price band.

In summary, schedules may be shared by user-defined product groups suchas those defined by price bands as well as by groups of stores such aspartitions by geography or zone or warehouse structure.

Creating an Optimization Scenario:

When optimizing a markdown plan, buyers usually focus on some keyparameters—the objective, the optimal number of markdown occasions, thetiming of each occasion, and the depth of price discount at eachoccasion. Looking at each in turn:

The preferred embodiment allows users to select both a primary andsecondary objectives used in selecting a markdown schedule. It alsoprovides a rules framework for implementing and maintaining variousbusiness strategies. The fundamental problem for the optimization is tofind the best markdown schedule given the user's objectives and rules.

The most common primary objective is to reduce inventory to some targetby the end of the markdown interval, and, secondarily, to do so at thegreatest profit possible. This pairing of competing objectives istypical—the first objective or primary objective is the goal of themarkdown plan, whereas the secondary objective usually defines thecriterion to pick a single optimal schedule from the number that maymeet the primary objective. Buyers also wish to maximize profit(primary) while reducing inventory (secondary) or maximizing revenue(secondary) or maximizing revenue (primary) at highest possible revenue(secondary) or lowest possible ending inventory (secondary). Unlike thePrice application, in the Markdown application the secondary constraintdoes not allow the user to choose a specific value that must be met.Rather it is assumed that the optimization will maximize the primaryobjective while keeping in mind that the user is also concerned aboutthe variable in the secondary constraint. To be precise, of thecompeting optimal schedules (with respect to the primary goal), theoptimization will pick the one that yields the best value of thesecondary goal. A specific example of a mark down system will beprovided in Part III Markdown System Example.

Buyers also set up several operating and business rules and constraintsby setting values on the parameters that define them. These include themaximum number of markdown occasions that can be run within the timeframe, the duration of the markdown cycle, and the maximum lifetimediscount. As noted earlier, companies usually establish over-archingdefaults for these rule parameters, which may then be modified orover-ridden by senior buyers or pricing organizations. The over-ridescan take place at multiple levels of the product hierarchy and atvarious organizational levels within the organization.

In addition to business rules for markdown parameters and attributes thepricing and promotion optimizations also involve other rules as well,principally those that govern a merchandizing strategy. In general,these rules were grouped into three distinct sets—a set of pricingrules, a set of interaction rules, and a set of markdown rules.

In an example scenario, once a buyer, for example Bartholomew, hascreated the optimization scenario, he dispatches it to the optimizationengine and takes the rest of the day off while the optimization worksaway at finding the optimal solution. It does this by searching throughall potential solutions, filtering out the ones that don't meet thebusiness and operational rules, evaluating the remaining candidates, andpicking the one with the highest objective value.

In order to gauge the worth or fitness of a markdown plan defined usingthe support tool 116, a buyer must be able to predict how it willperform if implemented. The optimization also requires this predictivecapability to evaluate and compare candidate solutions. Naturally, themetrics that capture the most relevant features of a markdown plan—itsexpected performance effectiveness financially and operationally dependon this capability and also constitute the content of the reports andcharts that buyers use to choose between competing markdown plans.

Once buyer Bartholomew has created a markdown plan through the supporttool 116 or via an optimization scenario, he uses different reports andcharts to evaluate the plan to determine if it is acceptable. Todetermine this, he has to assess different facets of performance of theplan. At the most granular level, the data for a plan consists of theprice and the predicted demand for each store and each week in themarkdown interval. Based on these two data elements, it is relativelyeasy to generate the predicted sales, the predicted ending inventory,the predicted revenue, the predicted profit (or loss) in each week foreach product. These elements, in turn, can be aggregated across weeks,products, and stores to compare and contrast the performance of a planby these dimensions. Buyers would like to be able to gain insightimmediately into aspects of the plan that are out-of-kilter. Forinstance, if a set of products share the same markdown schedule, theoverall results of the plan may be unduly affected by oneunderperforming product in the group. Buyers are always on the lookoutfor insightful reports that point out these issues. Similarly, one storeor a set of stores among a larger set may exhibit dramatically differentconsumer response to a markdown and warrant an alternative schedule.

Additionally, the roll-ups are useful in comparing one plan against acompeting one and choosing between them.

After the buyer has examined the results of one or more markdown plansand compared them to each other, he settles on one to approve with aview to implementing its recommendations in the stores. The optimizingsystem allows this process to be different for different retailers.

Operation

After the buyer has approved a markdown plan, the retailer's priceexecution systems receive the prices for implementation. Once the firstset of markdown prices is on the shelf, the operational phase ofmarkdown pricing begins in earnest. Since markdown cycles are usuallyvery short and dynamic, buyers monitor results regularly and makechanges to the approved plan if the actual performance and the predictedperformance are sufficiently different. They may also take action toensure that the larger goal of ensuring a smooth assortment transitionis being met.

A Senior Buyer may have multiple sets of products on markdown overdifferent sets of stores. Each combination of products and stores may beat different points in their respective markdown plans, but nonetheless,the buyer will often monitor the performance of all products under hispurview at the same time. Typically, this happens on a weekly basis andconsists of measuring the actual performance and comparing it againstforecasted performance. Besides measuring performance of the products onmarkdown, buyers also keep tabs on the status of the replacementproducts and whether they are on schedule to arrive as planned. Ifactual sales and the forecasted sales are wildly at odds or if thereplacement dates for products change, the buyer may take severalcorrective actions:

If the actual sales are considerably worse than expected the buyer maychoose to accelerate the timing of the next planed markdown and increasethe depth of the current markdown. These changes may occur at theproduct-store level, or at any higher level as deemed acceptable bycorporate guidelines, as follows. If the actual sales are higher thanforecasted, then the buyer may choose to delay the next plannedmarkdown. In some cases, buyers may order extra inventory to meet demandfor the product. Although this appears to be counter-intuitive to thestated purpose of markdown pricing which is to remove products from theassortment, such steps are sometimes necessary since exiting too quicklymay lead to thin assortment and narrow consumer choice in the periodbefore the replacement products arrive. If the arrival date forreplacement dates moves forward by a few days or weeks the inventory ofthe incoming products will build up rapidly, and therefore the buyer maychoose to accelerate the markdown of the obsolete product by bringingthe out-date forward and by increasing decreasing the price. Conversely,if the replacement date for an incoming product is delayed, buyer maychoose to slow down the markdown cycle for the existing product so thatstores have sufficient assortment choice until the new product arrives.These kinds of changes can also be rapidly evaluated using there-prediction/re-optimization feature that considers activities andoutcomes to date and suggests a new markdown schedule for the remainderof the weeks, observing all the original set of rules specified by theuser. It is important to note that some of these changes may be drivenby other merchandising activities (changes in promotions or prices ofrelated products) and the model explicitly takes this effect intoaccount.

A buyer may be responsible for anywhere from 5-10 products on markdownto over 500 at any one time. Some retailers obtain their measures ofperformance from legacy systems and from metrics that have been honedover years of use. Other retailers obtain this information from avariety of sources.

The actions that a buyer may take in response to performance metrics andchanges in replacement dates require him to decide on changes to themarkdown plan. As outlined above, these changes involve the timing anddepth of price changes as well as the time horizon of the proposed plan.The markdown product system flexibly allows the generation ofrecommendations every week that are approved automatically without abuyer's direct intervention or the generation of change recommendationsthat are first collected and scrutinized by a group of buyers andmarketing managers, who approve the recommendations that pass muster,and the rest are ignored.

Products that are on markdown are tagged as such at many retailers andtherefore buyers are able to filter products on markdown easily. Inaddition, they often refine this list further to view the performance ofproducts on their first markdown versus those on their second and so on.

The markdown cycle for a set of products ends on the day that the storesremove the them from the shelf. At this point, the left over inventorymay be disposed off by selling it to a third party, by returning it to awarehouse for future disposal, or, in some cases the inventory may bedestroyed.

MARKDOWN SYSTEM EXAMPLE Model Form

For markdown in this embodiment an econometric model is used, which usesthe univariate time-series approach to estimate demand curves within amarkdown period. Specifically, in this embodiment, the multiplicativeform of demand equation:

-   -   Sale(t)=Seasonality(t)*Product_life_cycle(t)*Price(t)*Err(t)    -   where Sale(t)=sales in equivalent units in time period t.        -   Seasonality(t)=seasonality index in time period t        -   Product_life_cycle(t)=product life cycle index in time            period t.        -   Price(t)=price effect in time period t.        -   Err(t)=random error in time period t.

The sales model is fit in four steps:

-   -   -   Seasonality estimation        -   Price elasticity and Product_life_cycle estimation        -   Clustering of related items, if necessary        -   Product_life_cycle estimation, if necessary

STAGE 1: SEASONALITY ESTIMATE

First, seasonality is estimated using well known techniques thataccounts for seasonal changes in the demand. In a preferred embodiment,products may be grouped into different sets based upon theircharacteristics including (but not limited to) sales profile, level ofaffinity between products, price elasticity of the products, promotionresponses of the product etc. These seasonality profiles are propagatedinto the future and are also used for new upc's during the markdownperiod.

In a preferred embodiment, seasonality profiles can be created using thefollowing equation:

$\begin{matrix}{{\ln \mspace{11mu} S_{i,s,t}} = {\sum\limits_{k = 1}^{52}\; {\vartheta_{k}X_{t,k}}}} & (1)\end{matrix}$

where X_(t,k) =δ_(t,k) (Kronecker delta, which is 1 only when t=k and 0otherwise) and υ_(k) is the common seasonality for all items in thecluster.

$\vartheta_{k} = {\frac{1}{N_{upc}N_{stores}}{\sum\limits_{i,s}\; {\ln \mspace{11mu} S_{i,s,t}}}}$

The seasonality coefficient for a given week corresponds to average (inlog units) sales of all upc's over all stores for that week.

STAGE 2: PRICE AND PRODUCT LIFE CYCLE ESTIMATES

At the second stage, price elasticity and product life cycle terms areestimated for each upc. These terms are well defined in econometricsliterature and as expected, many variants are available in the researchliterature. In the preferred embodiment, PLC(t) and Price(t) effects maybe estimated in the following manner:

$\begin{matrix}{{\overset{\sim}{S}}_{i,s,t} = {{{PLC}_{i,s,t}\left( \frac{P_{\max,i,s}}{P_{i,s,t}} \right)}^{\gamma}{\exp \left( ɛ_{i,s,t} \right)}}} & (2)\end{matrix}$

The product life-cycle term (PLC_(i,s,t)) can again be represented inmany ways. In the preferred embodiment it is represented by thefollowing functional form:

$\begin{matrix}{{{PLC}_{i,s,t} = {\left( \frac{t_{i,s}}{T_{i,s}} \right)^{\alpha_{i}}\left( {1 - \frac{t_{i,s}}{T_{i,s}}} \right)^{\beta_{i}}}},{t_{i,s} = 1},\ldots \mspace{14mu},T_{i,s}} & (3)\end{matrix}$

where α_(i) and β_(i) are the parameters to be estimated, T_(i,s) is thetotal length of time item i is sold in store s. The PLC value wasdefined to be zero during weeks before the item was introduced and afterit was removed. T_(i,s) is the expected out-date for product i and isassumed to be known a priori (e.g. It could be provided by the userbased upon their specific plans or the knowledge. In absence of anyrecommendation, a default value; may be used for example the life-spanof products in a category. P_(max,i,s) is the maximal price observed fora given upc-store combination throughout the sales history. The errortermε_(i,s,t) was assumed to be normally distributed. However,extensions to non-normal errors can also be easily constructed.

Taking log transformations, the final stage 2 model takes the followingform:

$\begin{matrix}{{\ln \left( {\overset{\sim}{S}}_{i,s,t} \right)} = {q_{i} + {\alpha_{i}\mspace{11mu} {\ln \left( \frac{t_{i,s}}{T_{i,s}} \right)}} + {\beta_{i}\mspace{11mu} {\ln \left( {1 - \frac{t_{i,s}}{T_{i,s}}} \right)}} + {\gamma_{i}\mspace{11mu} {\ln \left( \frac{P_{\max,i,s}}{P_{i,s,t}} \right)}} + ɛ_{i,s,t}}} & (4)\end{matrix}$

The user can tune the econometric model in multiple ways. Some of thespecific embodiments would include:

-   -   Specifying time period for model training    -   Specifying the set of products and set of stores over which to        train the model    -   Specifying interactions between the products to account for in        the model training.

The Optimization model comprises a demand equation for determiningdemand, an inventory equation that is based on previous inventory minussales, an initial sales-inventory balance equation, a sales-inventorybalance equation, a sales demand balance equation, and a pricedefinition equation. In an implementation of the invention, the user isable to select one primary objective and one secondary objective. Theobjectives include Maximize Profit, Maximize Revenue, and MinimizeInventory. This section will describe the formulation for eachcombination of objectives.

Given a product with a time horizon of t=0, . . . , T and an initialinventory of I_(i,s,0), the optimization will try to identify an optimalmarkdown schedule {d_(i,s,t):0≦t≦T}, where d_(i,s,t) refers to thepercentage discount applied to the initial product price at time t andstore s for each markdown. Thus, the resulting price in period t isdenoted by P_(i,s,t) and is calculated asP_(i,s,t)=P_(i,s,0)(1−d_(i,s,t)).

Optimization Objectives:

In this section, different objectives available to the users and howthey are implemented in the preferred embodiment are listed. Since theapplication supports both a primary and a secondary objective, examplesare based upon these combinations. Note that choosing only a primaryobjective is same as selecting the same goal for the primary andsecondary objectives (e.g. Just profit objective is same as profitprimary objective and profit secondary objective).

Profit-Profit Objective:

Max(S _(i,s,t) P _(i,s,t)−(I _(i,s,0) C _(i,s))−(I _(i,s,t) C _(i,s)CoC)+(I _(i,s,T) SV _(i,s)))

,where the subscript s refers to the store dimension and i for theproduct dimension and t for the time dimension. It should be noted thatthe same objective applies for simply having profit as the primaryobjective. I_(i,s,0) represents the initial inventory at for product Iin store s. C_(i,s) is the unit product cost to the retailer for aproduct i at store s. CoC is called the cost of capital and representsthe opportunity cost of having money tied up in inventory. SV_(i,s) iscalled the Salvage Value and represents the recovery value of unsoldinventory at the end of optimization period.

Revenue-Revenue Objective:

Max(S _(i,s,t) P _(i,s,t)+(I _(i,s,T) SV _(i,s)))

When the user picks revenue objective (or revenue primary and revenuesecondary objectives) , above equation is used for calculating revenues.Note that it does not include costs as in the previous case.

Inventory-Inventory Objective:

Max(S _(i,s,t) P _(i,s,t))

If the user selects a primary goal of minimizing inventory and asecondary constraint of inventory or null, then they will be solving theInventory Objective above. It should be noted that the intent of theinventory objective is to minimize inventory on hand or to maximizesales volume.

Mixed primary-secondary objectives:

When the user selects different primary and secondary objectives, thenthe optimization engine tries to identify a schedule that accounts forboth, implicitly giving greater weight to primary goal and less tosecondary goal. Although many different approaches can be taken toachieve this goal, in our preferred embodiment this is achieved byintroducing additional terms in the objective function to reflectblending of different goals.

Profit-Inventory Objective or Revenue-Inventory:

Max(S _(i,s,t) P _(i,s,t)−(I _(i,s,0) C _(i,s))−(I _(i,s,t) C _(i,s)CoC))

The purpose of this objective is to select a schedule that chooses thebest tradeoffs from a gross margin point of view while simultaneouslyconsidering the costs of holding inventory. Including the inventorycosts explicitly will result into deeper markdowns than the case of justthe profit objective.

Other combinations are applied in a similar manner.

The Econometric Model

Optimization engine uses the same econometric model as laid out earlierto determine demand forecasts based upon applicable conditions.

Inventory balance equation:

I _(i,s,t) =I _(i,s,t−1) −S _(i,s,t)

This equation just says that the inventory for this period is equal tothe ending inventory from last period minus this week's sales.

Sales Equation:

S_(i,s,t)=min{D _(i,s,t) , I _(i,s,t−1)}

This equation ensures that actual sales are constrained both by thedemand and inventory levels. In particular, the sales are set to be theminimum of these two levels in a given week. Note that D_(i,s,t)represents the demand for product i in store s and time t.

Price Trend Definitions:

P _(i,s,t) ≦P _(i,s,t−1)

Where P_(i,s,t) represents the selling price for product p in stores sin week t. This equation ensures that prices are strictlynon-increasing. In other words, the selling price is not allowed to goup in any of the weeks when compared to the previous week as this is afundamental requirement for markdown pricing.

General Constraints on Markdown Schedules Only One Markdown per WeekEquations:

This equation ensures only one markdown occasion per week is applied fora given storegroup-productgroup combination (from a set of all theallowable markdown points).

Pricing Rules Maximum Weekly Discount Bounds:

This constraint limits any new markdown occasion to have a discount ofless than MaxPctChangeM(sg,pg)—a value provided by the user. This valueis a percentage and is hence constrained to be between 0 and 100. Thereare multiple reasons that would require that this value be set to anumber such as 66%. This value can be specified separately for each(store, product) combination or for a group of stores and a group ofproducts.

Minimum Weekly Discount Bounds:

This constraint requires that if a discount is taken in a given week, itshould be larger than MinIncPctChangesg,pg, again a percentage valueprovided by the user. Again, there are a number of reasons for requiringthis behavior, including business and legal reasons. Like in theprevious case, this can be specified separately for each (store,product)combination or for a group of stores and a group of products.

Timing Rules Minimum Number of Weeks Till First Markdown Bounds:

This allows the user to choose the first allowable occasion for taking amarkdown. This value is specified as number of weeks and is interpretedto be the number of weeks from the beginning of scenario period duringwhich a markdown may not be taken.

Maximum Number of Markdown Occasions Constraints:

This constraint helps the user to control maximum number of markdownoccasions and is provided by the user as an integer number.

Time Gap Separation Constraints:

This allows the user to specify the time gap between two consecutivemarkdown occasions. Parameter gap(sg,pg) is used to capture this inputfrom the user.

Other Business Rules Minimum Inventory for a Markdown Bounds:

This gives the user control over the markdown behavior and instructs theoptimization engine to not take any markdowns for (store,product) or(store-group, product-group) combinations where the inventory levelfalls below the user specified level. One of the reasons for thisrequirements is the fact that very low inventory numbers tend to beunreliable and do not provide a good opportunity for realizing gains.

Markdown budget dollars

This constraint will limit the markdowns to ensure that the markdownbudget is observed. For example, a user can specify the markdown budget(in absolute dollars or in terms of profit margins) that has to beobserved across a set of products. In a specific example, the user mayset a markdown budget of $1 million for a category and across the chain.

Rule Hierarchy and Rule Relaxation:

In the preferred embodiment, the application enables users to specify anumber of different business rules. At times, not all of these rules canbe satisfied. A number of different methods may be used to address thissituation. In a preferred embodiment, the optimization engine supports auser-defined hierarchy of rules. This ensures that the most importantrules get satisfied before less important ones. Further, in case ofconflicts, this same hierarchy is used to relax lower priority rules toensure compliance with higher priority rule. Another feature of thismethod is that it merely relaxes rules rather than dropping them.

During an optimization, it may be found that a rule is infeasible. Arule is determined to be infeasible, if either it cannot be satisfiedwhen all other rules are satisfied or if an optimization cannot beperformed without violating the rule. One method of dealing with aninfeasible rule is to drop the rule found to be infeasible.

For example, the user defines a rule that requires a minimum gap of 3weeks between markdowns. Then, the user defines another rule (at thelowest priority) that specifies the minimum discount amount in case of amarkdown be 20%. In addition, there may be other (higher priority) rulesthat may cause the above two rules to be infeasible. In this embodiment,we might relax the lowest priority rule to allow markdown discounts of15% (instead of the 20% as initially stipulated).

FIG. 5 is a flow chart of a preferred embodiment of a rule relaxationprocess. The rules are prioritized (step 504). A default prioritizationmay be provided, with an interface, which may allow a user to change theprioritization from the default. A check is made to see if a rule isinfeasible (step 508). A rule is deemed to be infeasible if therelationship expressed by the rule is not able to be satisfied. Forinstance a rule X+Y<10, is violated for when X is 7 and Y is 7 sincethen X+Y=14 which is greater then 10. If no rule is found to beinfeasible, the rule relaxation process is stopped (step 528). If atleast one rule is found to be infeasible, the lowest priority infeasible(LPI) rule is found (step 512). A determination is made whether ruleswith lower priorities than the priority of the LPI rule may be relaxedto allow the LPI rule to become feasible (step 516). In the preferredembodiment, the lower priority rules are checked before higher priorityrules. If it is found that rules with lower priorities than thatpriority of the LPI rule may be relaxed to a point that allows the LPIrule to be come feasible, then these rules with lower priorities arerelaxed incrementally so that the LPI rule becomes feasible (step 520).In the preferred embodiment, lower priority rules are relaxed beforehigher priority rules. If it is found that rules with lower prioritiesthan the priority of the LPI rule cannot be relaxed to allow the LPIrule to become feasible, then the LPI rule is relaxed until it becomesfeasible (step 524). The rules are then rechecked to see if there areany remaining rules that are infeasible (step 508). This process iscontinued until all rules are feasible.

In the preferred implementations, the specific infeasible solution atwhich the optimization process stops will depend on the optimizationalgorithm and the initial starting point. This means that specific rulesthat are relaxed in order to get to feasibility depend on both theoptimization algorithm and the starting values These examples are merelyillustrative and are not meant to limit the scope of the invention. Inaddition, rule priority was assigned by rule category. In otherembodiments the rule priority may be assigned to individual rulesinstead of to rule categories.

Other specific methods of rule relaxation may be used. In general, rulerelaxation methods provide for the prioritization of rules and causelower priority rules to be relaxed before relaxing higher priority rulesin order to make all resulting rules feasible. Another specificembodiment of this general rule relaxation method may allow differentcombinations of lower priority rules to be relaxed. These lower priorityrules may be relaxed at the same rate or at different rates until thehigher priority rule becomes feasible. Different combinations of therelaxation of lower priority rules may be specified in differentembodiments.

If the user selects Profit as their primary goal and either Inventory orRevenue as their secondary constraint, then the optimization will solvethe Profit-Inventory objective. The purpose of this objective is toselect a schedule that chooses the best tradeoffs from a gross marginpoint of view while simultaneously considering the costs of holdinginventory. The hope is that the inventory costs will increase the salesvolume early in the schedule.

Various constraints may be used, such as minimum and maximum pricediscounts for markdown, timing between markdowns, maximum number ofmarkdowns, periods between markdowns, and minimum amount of inventory.

While this invention has been described in terms of several preferredembodiments, there are alterations, permutations, and substituteequivalents, which fall within the scope of this invention. It shouldalso be noted that there are many alternative ways of implementing themethods and apparatuses of the present invention. It is thereforeintended that the following appended claims be interpreted as includingall such alterations, permutations, and substitute equivalents as fallwithin the true spirit and scope of the present invention.

1. An apparatus for providing relative optimized pricing for a pluralityof markdown items for at least one store, comprising: a financial enginefor determining revenue for determining sales costs; an optimizationengine receiving input from the financial engine for using thedetermined sales costs and providing relative optimized pricing formarkdown items and provides pricing and a promotion calendar fornon-markdown items, comprising an engine for providing non-markdown itempricing; an engine for providing a promotion calendar; and an engine forproviding markdown item pricing.
 2. The apparatus, as recited in claim1, further comprising an econometric engine for determining demandcoefficients and providing the determined coefficients to theoptimization engine.
 3. The apparatus, as recited in claim 2, furthercomprising a computer system on which the financial engine, econometricengine, and optimization engine are implemented.
 4. The apparatus, asrecited in claim 3, wherein the financial engine generates cost data. 5.The apparatus, as recited in claim 4, further comprising a support toolwith a graphic user interface for specifying markdown price constraintsand providing the constraints to the optimization engine.
 6. Theapparatus, as recited in claim 5, wherein the graphic user interfacefurther comprises a graphical user interface for designating markdownitems and setting markdown rules.
 7. The apparatus, as recited in claim4, wherein the support tool allows the implementation of the plan. 8.The apparatus, as recited in claim 4, wherein the support tool providesa tuning indicator that indicate when tune thresholds are met.
 9. Theapparatus, as recited in claim 4, wherein the support tool allows thespecification of restraint rules.
 10. The apparatus, as recited in claim9, wherein the support tool further allows the specification of ahierarchy for the restraint rules.
 11. The apparatus, as recited inclaim 9, wherein a restraint rule allows the specification of a minimumtime period between markdowns.
 12. The apparatus, as recited in claim11, wherein a restraint rule allows the specification of a minimum pricereduction.
 13. The apparatus, as recited in claim 12, wherein arestraint rule allows the specification of a unique last digit tosignify a markdown price reduction.
 14. The apparatus, as recited inclaim 13, wherein a restraint rule allows specification of markdownstart and end dates for a markdown product.
 15. The apparatus, asrecited in claim 14, wherein the restraint rules further allowsspecification of salvage value of a markdown product.
 16. The apparatus,as recited in claim 9, wherein the support tool allows the specificationof product groups and a hierarchy wherein product hierarchy is dependenton the product group in which a product is placed and restraint rulesare modified and applied according to the hierarchy.
 17. The apparatus,as recited in claim 16, wherein the support tool further allows thespecification of store clusters, so that the constraint rules aremodified and applied according to product hierarchy and store cluster.18. The apparatus, as recited in claim 4, wherein the support toolfurther allows the specification of a markdown budget constraint. 19.The apparatus, as recited in claim 4, wherein the support tool furtherallows the specification of a prediction, which provides predicted salesa relative optimized pricing.
 20. The apparatus, as recited in claim 19,wherein the support tool further allows the collection of data after thespecification of a prediction and determining and displaying on thesupport tool a degree of difference between the collection of data afterthe specification of a prediction and the prediction.
 21. The apparatus,as recited in claim 3, input for the econometric engine for receivingdata on at least a weekly basis, wherein the econometric engine providesoutput to the optimization engine on at least a weekly basis and whereinthe optimization engine revises the provided relative optimized pricingfor markdown items on at least a weekly basis.
 22. The apparatus, asrecited in claim 3, wherein the optimization engine receives datarelated to in-store and warehouse inventory levels and uses the in-storeand warehouse inventory levels to determine markdown prices.
 23. Theapparatus. as recited in claim 1, wherein the optimizing engine,comprises: computer readable code for storing a plurality of rules;computer readable code for allowing the prioritization of the pluralityof rules; and computer readable code for relaxing at least one lowerpriority rule to allow a higher priority rule to become feasible. 24.The apparatus, as recited in claim 1, wherein the optimization engineprovides relative optimized pricing by generating a price and promotionplan and then generating an optimized markdown plan based on thegenerated price and promotion plan.
 25. The apparatus, as recited inclaim 1, wherein the optimization engine manages conflicts betweenrelative optimized pricing for markdown items and pricing and promotionsfor non-markdown items
 26. A computer implemented method for providingpricing for markdown items and non-markdown items, comprising:collecting sales, cost, and competitive data from the plurality ofstores; analyzing the collected sales, cost, and competitive data;providing pricing and a markdown schedule for markdown items and pricingfor non-markdown items; analyzing on an at least weekly basis sales ofthe markdown items; tuning the markdown schedule based on the analyzingdata collected on an at least weekly basis; and providing tuned markdownschedule.
 27. The computer implemented method, as recited in claim 26,wherein the providing pricing and the markdown schedule further providesa promotion schedule, further comprising: analyzing on a weekly basissales of promotional items; tuning the promotion schedule; and providinga tuned promotion schedule.
 28. The computer implemented method, asrecited in claim 27, wherein the providing pricing and the markdowncalendar is performed after pricing of non-markdown items and promotionschedule has been optimized and uses the pricing of the non-markdownitems and promotion schedule.
 29. The computer implemented method, asrecited in claim 26, further comprising receiving user supplied markdownprice constraints.
 30. The computer implemented method, as recited inclaim 26, further comprising providing a tuning indicator, whichindicates when tuning thresholds are met.
 31. The computer implementedmethod, as recited in claim 26, further comprising receiving usersupplied constraint rules.
 32. The computer implemented method, asrecited in claim 31, wherein the constraint rules have a user definedhierarchy.
 33. The computer implemented method, as recited in claim 31,wherein a restraint rule allows the specification of a minimum pricereduction.
 34. The computer implemented method, as recited in claim 31,wherein a restraint rule allows the specification of a unique last digitto signify a markdown price reduction.
 35. The computer implementedmethod, as recited in claim 31, wherein a restraint rule allowsspecification of markdown start and end dates for a markdown product.36. The computer implemented method, as recited in claim 31, wherein therestraint rules allows specification of salvage value of a markdownproduct.
 37. An apparatus for providing relative optimized pricing formarkdown items, comprising: a financial engine for determining revenuefor determining sales costs; an optimization engine receiving input fromthe financial engine for using the determined sales costs and providingrelative optimized pricing for markdown items and provides pricing and apromotion calendar for non-markdown items.
 38. The apparatus, as recitedin claim 37, further comprising an econometric engine for determiningdemand coefficients and providing the determined coefficients to theoptimization engine.
 39. The apparatus, as recited in claim 38, furthercomprising a computer system on which the financial engine, econometricengine, and optimization engine are implemented.
 40. The apparatus, asrecited in claim 39, wherein the financial engine generates cost data.41. The apparatus, as recited in claim 40, further comprising a supporttool with a graphic user interface for specifying markdown priceconstraints and providing the constraints to the optimization engine.42. The apparatus, as recited in claim 41, wherein the graphic userinterface further comprises a graphical user interface for designatingmarkdown items and setting markdown rules.
 43. The apparatus, as recitedin claim 40, wherein the support tool allows the implementation of theplan.